Riding performance quantification method for motorcycles in terms of collision probability using the logit model

In recent years, many Advanced Driver Assistance Systems (ADAS) have been proposed and introduced under the development of sensing technology and the issue of driving safety. But many kinds of ADASs have a specific threshold to control the alarm or some support. This is decided based on the experime...

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Autores principales:	Joohyeong LEE, Saya KISHINO, Keisuke SUZUKI
Formato:	article
Lenguaje:	EN
Publicado:	The Japan Society of Mechanical Engineers 2020
Materias:	motorcycle riding performance advanced driver assistance system (adas) advanced rider assistance system (aras) logistic regression analysis evaluation method collision probability Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/e6205c9aad7c4203b90ac0174c963c2b
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id	oai:doaj.org-article:e6205c9aad7c4203b90ac0174c963c2b
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spelling	oai:doaj.org-article:e6205c9aad7c4203b90ac0174c963c2b2021-11-29T05:59:27ZRiding performance quantification method for motorcycles in terms of collision probability using the logit model2187-974510.1299/mej.20-00015https://doaj.org/article/e6205c9aad7c4203b90ac0174c963c2b2020-07-01T00:00:00Zhttps://www.jstage.jst.go.jp/article/mej/7/4/7_20-00015/_pdf/-char/enhttps://doaj.org/toc/2187-9745In recent years, many Advanced Driver Assistance Systems (ADAS) have been proposed and introduced under the development of sensing technology and the issue of driving safety. But many kinds of ADASs have a specific threshold to control the alarm or some support. This is decided based on the experimental or mathematical calculations in terms of the optimization of the human-machine interface of each system. But almost all of the systems (especially warning systems) have just a single threshold value to issue the warning, and the driving performance of drivers fluctuating in real time is not considered. In this study, we proposed a quantification method of riding performance and performed the logistic regression analysis for the collision prediction model based on riding performance to optimize the warning threshold of ADAS. For this study, 64 test subjects (Mean age = 22.14, S.D. = 3.71) participated in the experiments using simulator. Experiments were conducted for three risk events (left-angle collision when a rider was driving on priority road or driving on non-priority road, and right turning collision) and dummy events with the same road environment without risky situations. We proposed a quantification method of riding performance through the total sum of a product of the generalized value of riding behaviours. We also proposed the logit model, which can be constructed in terms of the collision probabilities and riding performance, which is quantified using our proposed method. In the logit model, collision occurrence was used as the dependent variable and riding performance was used as the independent variable for logistic regression analysis to clarify the condition where the probability of collision increases. Finally, we proposed a concept of the setting method of threshold value for the warning timing of ADAS according to the rider’s performance level based on collision probabilities during each riding performance.Joohyeong LEESaya KISHINOKeisuke SUZUKIThe Japan Society of Mechanical Engineersarticlemotorcycleriding performanceadvanced driver assistance system (adas)advanced rider assistance system (aras)logistic regression analysisevaluation methodcollision probabilityMechanical engineering and machineryTJ1-1570ENMechanical Engineering Journal, Vol 7, Iss 4, Pp 20-00015-20-00015 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	motorcycle riding performance advanced driver assistance system (adas) advanced rider assistance system (aras) logistic regression analysis evaluation method collision probability Mechanical engineering and machinery TJ1-1570
spellingShingle	motorcycle riding performance advanced driver assistance system (adas) advanced rider assistance system (aras) logistic regression analysis evaluation method collision probability Mechanical engineering and machinery TJ1-1570 Joohyeong LEE Saya KISHINO Keisuke SUZUKI Riding performance quantification method for motorcycles in terms of collision probability using the logit model
description	In recent years, many Advanced Driver Assistance Systems (ADAS) have been proposed and introduced under the development of sensing technology and the issue of driving safety. But many kinds of ADASs have a specific threshold to control the alarm or some support. This is decided based on the experimental or mathematical calculations in terms of the optimization of the human-machine interface of each system. But almost all of the systems (especially warning systems) have just a single threshold value to issue the warning, and the driving performance of drivers fluctuating in real time is not considered. In this study, we proposed a quantification method of riding performance and performed the logistic regression analysis for the collision prediction model based on riding performance to optimize the warning threshold of ADAS. For this study, 64 test subjects (Mean age = 22.14, S.D. = 3.71) participated in the experiments using simulator. Experiments were conducted for three risk events (left-angle collision when a rider was driving on priority road or driving on non-priority road, and right turning collision) and dummy events with the same road environment without risky situations. We proposed a quantification method of riding performance through the total sum of a product of the generalized value of riding behaviours. We also proposed the logit model, which can be constructed in terms of the collision probabilities and riding performance, which is quantified using our proposed method. In the logit model, collision occurrence was used as the dependent variable and riding performance was used as the independent variable for logistic regression analysis to clarify the condition where the probability of collision increases. Finally, we proposed a concept of the setting method of threshold value for the warning timing of ADAS according to the rider’s performance level based on collision probabilities during each riding performance.
format	article
author	Joohyeong LEE Saya KISHINO Keisuke SUZUKI
author_facet	Joohyeong LEE Saya KISHINO Keisuke SUZUKI
author_sort	Joohyeong LEE
title	Riding performance quantification method for motorcycles in terms of collision probability using the logit model
title_short	Riding performance quantification method for motorcycles in terms of collision probability using the logit model
title_full	Riding performance quantification method for motorcycles in terms of collision probability using the logit model
title_fullStr	Riding performance quantification method for motorcycles in terms of collision probability using the logit model
title_full_unstemmed	Riding performance quantification method for motorcycles in terms of collision probability using the logit model
title_sort	riding performance quantification method for motorcycles in terms of collision probability using the logit model
publisher	The Japan Society of Mechanical Engineers
publishDate	2020
url	https://doaj.org/article/e6205c9aad7c4203b90ac0174c963c2b
work_keys_str_mv	AT joohyeonglee ridingperformancequantificationmethodformotorcyclesintermsofcollisionprobabilityusingthelogitmodel AT sayakishino ridingperformancequantificationmethodformotorcyclesintermsofcollisionprobabilityusingthelogitmodel AT keisukesuzuki ridingperformancequantificationmethodformotorcyclesintermsofcollisionprobabilityusingthelogitmodel
_version_	1718407617874755584

Riding performance quantification method for motorcycles in terms of collision probability using the logit model

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